A nanosilver-based spectrophotometric method for determination of malachite green in surface water samples.

A new spectrophotometric method is reported for the determination of nanomolar level of malachite green in surface water samples. The method is based on the catalytic effect of silver nanoparticles on the oxidation of malachite green by hexacyanoferrate (III) in acetate-acetic acid medium. The absorbance is measured at 610 nm with the fixed-time method. Under the optimum conditions, the linear range was 8.0 × 10(-9)-2.0 × 10(-7) mol L(-1) malachite green with a correlation coefficient of 0.996. The limit of detection (S/N = 3) was 2.0 × 10(-9) mol L(-1). Relative standard deviation for ten replicate determinations of 1.0 × 10(-8) mol L(-1) malachite green was 1.86%. The method is featured with good accuracy and reproducibility for malachite green determination in surface water samples without any pre-concentration and separation step.

Application of genetic algorithm-kernel partial least square as a novel non-linear feature selection method: partitioning of drug molecules.

Genetic algorithm (GA) and partial least squares (PLS) and kernel PLS (KPLS) techniques were used to investigate the correlation between immobilized liposome chromatography partitioning (log Ks) and descriptors for 65 drug compounds. The models were validated using leave-group-out cross validation LGO-CV. The results indicate that GA-KPLS can be used as an alternative modelling tool for quantitative structure-property relationship (QSPR) studies.

pK(a) modelling and prediction of drug molecules through GA-KPLS and L-M ANN.

Genetic algorithm and partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between dissociation constant (pK(a) ) and descriptors for 60 drug compounds. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of models. Descriptors of GA-KPLS model were selected as inputs in L-M ANN model. The results indicate that L-M ANN can be used as an alternative modeling tool for quantitative structure-property relationship (QSPR) studies.

New kinetic-spectrophotometric method for monitoring the concentration of iodine in river and city water samples.

A new kinetic method has been developed for the determination of iodine in water samples. The method is based on the catalytic effect of I(-) with the oxidation of Indigo Carmine (IC) by KBrO(3) in the sulfuric acid medium. The optimum conditions obtained are 0.16 M sulfuric acid, 1 × 10(-3) M of IC, 1 × 10(-2) M KBrO(3), reaction temperature of 35°C, and reaction time of 80 s at 612 nm. Under the optimized conditions, the method allowed the quantification of I(-) in a range of 12-375 ng/mL with a detection limit of 0.46 ng/mL. The method was applied to the determination of iodine in river and city water samples with the satisfactorily results.

Prediction of polar surface area of drug molecules: a QSPR approach.

A quantitative structure-property relationship (QSPR) study based on an artificial neural network (ANN) was carried out for the prediction of the microemulsion liquid chromatography polar surface area (PSA) of a set of 32 drug compounds. The genetic algorithm-kernel partial least squares (GA-KPLS) method was used as a variable selection tool. A KPLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient Q(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-property relationships were followed by nonlinear approach using artificial neural networks and consequently better results were obtained. Also this demonstrates the advantages of ANN.

Application of artificial neural network to predict the retention time of drug metabolites in two-dimensional liquid chromatography.

Genetic algorithm and partial least square (GA-PLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention time and descriptors for drug metabolites which obtained by two-dimensional liquid chromatography. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of four models. Both methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R(2) value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA-PLS models.

Ultra-sensitive quantification of sub-nanomolar levels of iodine in blood serum samples by kinetic-spectrophotometric method.

A simple and sensitive kinetic-spectrophotometric method is developed for the determination of trace amounts of iodine in blood serum samples based on its catalytic effect on the oxidation of Nile Blue A by potassium bromate in sulfuric acid medium and at 25°C. The absorbance is measured at 595.5 nm with the fixed-time method. The optimization of the operating conditions regarding concentration of the reagents, temperature, and interferences are also investigated. The calibration curve is linear over the concentration range between 20.0 to 500.0 ng ml(-1) of iodine with good precision and accuracy. The detection limit of the method is down to 12.0 ng ml(-1). The relative standard deviation for a standard solution of 100.0 ng ml(-1) of iodine is 1.32% (n = 10). The proposed method provides a highly sensitive, selective, and relatively rapid assay for iodine at ultra trace level without any pre-concentration and separation step. The method was applied to the determination of iodine in blood serum samples. The analytical results of the real samples were in excellent agreement with standard method.